Uncertainty-Diversity Ranking Coreset Selection for Efficient Spam Detection

Efficient spam detection in resource-constrained environments remains challenging due to class imbalance, noisy text, and the computational demands of large Transformer models. We introduce a novel coreset selection framework based on a unified Uncertainty-Diversity Ranking (UDR), which explicitly combines predictive uncertainty with representativeness to prioritize highly informative samples while ensuring diversity and class balance. Our method supports multiple coreset strategies, including Top-K, Bottom-K, and adaptive class-wise selection, enabling robust performance even with a fraction of the training data. Extensive experiments on benchmark datasets, including UCI SMS, UTKML Twitter, and Ling-Spam, show that UDR maintains or improves accuracy, precision, and recall while reducing training data by up to 95\%, significantly lowering computational cost. These results demonstrate the potential of UDR in resource-limited settings.